Consumption of news and entertainment content looks significantly different today than two decades ago when viewers watched mainly television programming. Today, they use mobile phones, smart TVs, cable TVs, laptops or tablets to consume content. Statista reports that the average U.S. adult spends around eight hours consuming digital media across devices each day. Consumer preferences have also evolved like the content; viewers want personalized feeds and on-demand experiences.
This presents a massive opportunity for media and entertainment (M&E) companies as they get access to volumes of data related to consumer behavior and engagement patterns. Media companies now know how their viewers are consuming content at every touchpoint, from specific platforms or types of content to the time and duration of viewing.
However, access to enormous consumer data also leads to greater complexity and fragmentation. Data is stored on many platforms and devices, a factor that also prevents its efficient use. For example, a sports television channel has information on the different kinds of sports being watched more on TV but may lack insights about how the same viewers are consuming similar content on gaming platforms, or mobile applications.
This fragmentation leads to an incomplete understanding of the audience and missed opportunities of targeted marketing for media and entertainment companies. They need viewership data at scale if they want to use data accurately for decision-making and compete in an ever-changing entertainment scenario.
What’s Preventing Viewership Analytics at Scale?
Valuable data is dispersed across not only different platforms and devices but also across teams. For instance, the marketing team tracks the type of content people watch, while the sales team monitors ad views and revenue. These teams don’t always share their data or have access to tools that enable interaction, creating data silos. In addition, fragmented data is often inaccurate or incomplete and cannot be relied upon for insights.
This makes the process of searching or analyzing slow, resulting in lost opportunities to monetize audience engagement based on real-time insights. Even AI tools become ineffective because data is either missing or stored in a different format. AI tools need well-organized and clean data to produce relevant insights.
An AI-Powered Unified Semantic Platform is the Solution
A logical solution is to consolidate all the data into one unified system, with a semantic layer on top that provides meaning, context and consistency to raw data. Data from across devices, systems and platforms is linked under one semantic architecture that follows a single business logic to define and analyze data. The semantic layer also categorizes and catalogues terminology, ensuring that all the teams use the same language while querying data.
Data itself has many layers. There is data with unique values, like millions of unique customer log-in IDs or specific genres of content, known as high cardinality data. Then there’s historical data collected over many years or decades. When it comes to processing and analyzing data, the challenge is twofold: handle the volume and type of data and maintain high performance.
Unified Semantic Layer in M&E: Reaping the Benefits
An AI-powered unified system with a cloud architecture is built to process billions of data rows at high speed. AI algorithms and indexing aspects of the semantic layer accelerate the speed of specific queries. AI/ML capabilities also group and condense data into high-speed AI-powered aggregates by learning usage patterns to provide faster analysis within minutes and seconds. Moreover, natural language interfaces that use generative AI let different business users with no technical skills query data using plain human language.
The unified layer is an efficient tool for non-technical users (such as content teams, marketing teams) to explore and understand how their campaigns are performing. They can know details such as time stamps of drop-offs in video content, popularity peaks or the most re-watched parts, total watch time and viewer engagement trends. With a unified platform, business users also have contextual analytics to make better promotion decisions and create efficient investment strategies. They have specific insights on cross-platform performance, viewer journey based on demographics and behavior, real-time content peak maps and revenue attribution analytics.
AI algorithms dig into multiple layers of viewer data—such as demographics, preferred devices, type of content, viewing time, etc.—to segment users and identify high-value viewers who are more likely to subscribe or engage with its content.
Analytics tools with natural language access allow business heads to get instant and accurate answers, enabling them to make decisions faster than competitors. Leaders do not need to wait for the analytics team to send detailed reports but can adjust plans based on daily, weekly or quarterly summaries of important business parameters.
Unified systems also empower advertisers by providing access to ad impressions based on demographic, device and content types. By measuring reach and frequency, the system prompts advertisers to adjust their campaigns or price points.
Conclusion
When data is unified and aggregated through AI-powered, unified platforms, it makes way for smarter content decisions. A unified data analytics system makes personalized recommendations and ad revenue optimization possible. It is particularly critical for media and entertainment companies which must remain responsive to evolving customer behavior and preferences.